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AGV scheduling optimization method based on two-stage multi-population parallel genetic algorithm

A genetic algorithm and optimization method technology, applied in the field of AGV car scheduling optimization, can solve problems such as slow calculation speed and premature convergence, and achieve the effects of increasing convergence speed, reducing costs, and improving economic benefits

Inactive Publication Date: 2017-10-20
QUANZHOU INST OF EQUIP MFG
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Problems solved by technology

However, these methods often have problems such as premature convergence and slow calculation speed, which bring certain limitations to the actual engineering application of optimization methods.

Method used

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  • AGV scheduling optimization method based on two-stage multi-population parallel genetic algorithm
  • AGV scheduling optimization method based on two-stage multi-population parallel genetic algorithm
  • AGV scheduling optimization method based on two-stage multi-population parallel genetic algorithm

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Embodiment Construction

[0053] Such as figure 1 As shown in this embodiment, an AGV trolley scheduling optimization method based on a two-stage multi-population parallel genetic algorithm is disclosed, which specifically includes the following steps:

[0054] AGV trolley scheduling is described in the logistics sorting task as: Assuming that each sorting station has only one sorter, each sorter can only sort one kind of goods at the same time, and each AGV trolley can go to multiple To pick up goods locally, each AGV car needs to transport the goods on the car to different sorting stations. The goal of AGV scheduling optimization is to minimize the time required to complete the specified sorting of goods.

[0055] The optimization problem of the AGV scheduling model is:

[0056] min{max(T 1 , T 2 ,...,T k ,...,T L )}

[0057] T k =(d ik -r ik )+H k ×s k ×t ii'k +H k ×s k ×t ijk +(d jk -r jk )+H k ×s k ×t jj'k +H k ×s k ×t jik

[0058]

[0059]

[0060]

[0061]

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Abstract

An AGV scheduling optimization model is built with goods cabinets, sorting tables and AGVs as scheduling research objects and minimum time for AGVs to complete a specified task as an optimization objective. The AGV scheduling optimization model is solved using a two-stage multi-population parallel genetic algorithm and a simulated annealing algorithm, and the algorithms are solved by means of parallel computation to get an optimal scheduling scheme of AGVs for a specified task. Populations can be prevented from converging too early. The computational efficiency is improved. The algorithms can deal with a large-scale scheduling optimization problem.

Description

technical field [0001] The invention belongs to the field of scheduling optimization, in particular to an AGV trolley scheduling optimization method based on a two-stage multi-population parallel genetic algorithm. Background technique [0002] The AGV trolley scheduling problem widely exists in production practice, especially the logistics scheduling of e-commerce, which is a typical problem in scheduling problems and has important research value and practical application significance. The problem description of AGV trolley scheduling is: to transport N commodities to m locations, the main task of scheduling is how to arrange the quantity of goods at each processing location, how to determine the type and quantity of commodities placed on the AGV trolley, so that the constraints can be Satisfies and optimizes several concerned goals, such as minimizing task time. This problem is also called the scheduling problem of parallel machines. The methods to solve this problem usu...

Claims

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Application Information

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IPC IPC(8): G06Q10/08
CPCG06Q10/08355
Inventor 陈豪张景欣陈松航张丹蔡品隆王耀宗
Owner QUANZHOU INST OF EQUIP MFG
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